Every agent deployment we've seen has the same problem.

The agent ships. It fails. The team patches the prompt, updates the retrieval, adds another guardrail. The agent fails again - differently, but just as predictably. The failures are logged. Nobody uses the logs. Nothing actually learns.

At small scale this is annoying. At the scale enterprises are moving toward, it becomes the reason AI initiatives stall.

The missing piece isn't better models. It isn't better prompts. It's a mechanism for agents to accumulate experience - to know what went wrong last time, in a situation like this one, and not repeat it.

That's what we're building. Infrastructure that gives agents structured memory from their own production history, so that every failure makes the next decision better. Agents that get more reliable with every deployment, not just more patched.

The goal is simple: agents that never make the same mistake twice.